Exam Professional Data Engineer topic 1 question 71 discussion

Correct Answer : A Entity analysis -> Identify entities within documents receipts, invoices, and contracts and label them by types such as date, person, contact information, organization, location, events, products, and media. Sentiment analysis -> Understand the overall opinion, feeling, or attitude sentiment expressed in a block of text. -- Avoid Custom models

should be A

Call the Cloud Natural Language API from your application. Process the generated Entities Analysis as labels. The Cloud Natural Language API is a pre-trained machine learning model that can be used for natural language processing tasks such as entity recognition, sentiment analysis, and syntax analysis. The API can be called from your application using a simple API call, and it can generate entities analysis that can be used as labels for the user's blog posts. This would be the quickest and easiest option for your team since it would not require any machine learning expertise or additional developer resources to build and train a model. Additionally, it will give you accurate and up-to-date results as the API is constantly updated by Google.

For the first time, the answer in exam topics matches community vote :-).

Of course the answer is A. Since the problem already states that you don't have time, resources or expertise. So the best solution in the case is to utilize the available API. Also since we need to extract the labels and not the sentiment of the text, we'll go for option A and not B

A. Call the Cloud Natural Language API from your application. Process the generated Entities Analysis as labels. The Cloud Natural Language API is a pre-trained machine learning model that can be used for natural language processing tasks such as entity recognition, sentiment analysis, and syntax analysis. The API can be called from your application using a simple API call, and it can generate entities analysis that can be used as labels for the user's blog posts. This would be the quickest and easiest option for your team since it would not require any machine learning expertise or additional developer resources to build and train a model. Additionally, it will give you accurate and up-to-date results as the API is constantly updated by Google.

Answer is A Call the Cloud Natural Language API from your application. Process the generated Entity Analysis as labels. Entity analysis -> Identify entities within documents receipts, invoices, and contracts and label them by types such as date, person, contact information, organization, location, events, products, and media. Sentiment analysis -> Understand the overall opinion, feeling, or attitude sentiment expressed in a block of text.

A is the answer. https://cloud.google.com/natural-language/docs/analyzing-entities Entity Analysis inspects the given text for known entities (proper nouns such as public figures, landmarks, etc.), and returns information about those entities.

Apparently, there is unanimity on answer [A] What if there was another available answer in an actual exam? E. Call the Cloud Natural Language API from your application. Process the generated Content Classification as labels What would you choose, A or E? My opinion is that Content Classification is more suitable for detecting subject.

A is the right one . Doc says: Entity analysis inspects the given text for known entities (Proper nouns such as public figures, landmarks, and so on. Common nouns such as restaurant, stadium, and so on.) and returns information about those entities. Entity analysis is performed with the analyzeEntities method.

Vote for A

a is correct

A. CD don't work as it requires Machine Learning experience. B - Sentiment Analysis is to analyze attitude, opinion, etc. So A.

Vote for A

A is correct

Answer: A Description: As time is less, use cloud NLP and entity is used to label general subjects, sentiment label for sentiment analysis